#Load packages
pacman::p_load(plotly, ggstatsplot, tidyverse)
#Import data
flatprice <- read_csv("data/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv", show_col_types = FALSE)Take-home Exercise 3
Visual Analytics of Resale Prices of Singapore Public Housing Properties
1. Overview
This exercise aims to uncover the salient patterns of the resale prices of public housing property by residential towns and estates in Singapore using appropriate analytical visualisation techniques. The visualization is designed using ggplot2, its extensions, and tidyverse packages.
The original dataset was downloaded from Data.gov.sg titled Resale flat princes based on registration date from Jan-2017 onwards.
The file downloaded was resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv
The focus of the study is on 3-ROOM, 4-ROOM and 5-ROOM types for 2022 period.
2. Data Preparation
#Data preparation
#filter for 2022 and 3-ROOM, 4-ROOM, 5-ROOM
#mutate remaining_lease to years
flatpriceclean <- flatprice |>
filter(flat_type %in% c('3 ROOM','4 ROOM','5 ROOM')) |>
mutate(year = as.integer(format(as.Date(paste(month, "-01", sep="")), "%Y")),
month = as.integer(format(as.Date(paste(month, "-01", sep="")), "%m")),
.before = 1)|>
filter(year == 2022) |>
mutate(remaining_lease_years = round((as.numeric(str_extract(remaining_lease, "^[0-9]+")) +
ifelse(is.na(as.numeric(str_extract(remaining_lease, " [0-9]+"))), 0, as.numeric(str_extract(remaining_lease, " [0-9]+")))/12), digits = 1),
resale_price_persqm = round(resale_price/floor_area_sqm, digits = 1),
lease_commence_date = as.integer(lease_commence_date),
.after = remaining_lease)
flatpriceclean# A tibble: 24,374 × 14
year month town flat_t…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶
<int> <int> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <int>
1 2022 1 ANG MO KIO 3 ROOM 320 ANG MO… 07 TO … 73 New Ge… 1977
2 2022 1 ANG MO KIO 3 ROOM 225 ANG MO… 07 TO … 67 New Ge… 1978
3 2022 1 ANG MO KIO 3 ROOM 331 ANG MO… 07 TO … 68 New Ge… 1981
4 2022 1 ANG MO KIO 3 ROOM 534 ANG MO… 07 TO … 82 New Ge… 1980
5 2022 1 ANG MO KIO 3 ROOM 578 ANG MO… 04 TO … 67 New Ge… 1980
6 2022 1 ANG MO KIO 3 ROOM 452 ANG MO… 01 TO … 83 New Ge… 1979
7 2022 1 ANG MO KIO 3 ROOM 560 ANG MO… 01 TO … 67 New Ge… 1980
8 2022 1 ANG MO KIO 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979
9 2022 1 ANG MO KIO 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979
10 2022 1 ANG MO KIO 3 ROOM 560 ANG MO… 10 TO … 67 New Ge… 1980
# … with 24,364 more rows, 4 more variables: remaining_lease <chr>,
# remaining_lease_years <dbl>, resale_price_persqm <dbl>, resale_price <dbl>,
# and abbreviated variable names ¹flat_type, ²street_name, ³storey_range,
# ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date#Check for missing values
any(is.na(flatpriceclean))[1] FALSE3. Visualization
3.1 Exploratory Data Visualization
The first plot purpose is to provide preliminary insight on the resale price of property vs remaining lease. the plot looks very cluttered as the number of dataset is high, however, this is deemed to be sufficient for preliminary analysis. Note that the resale price is normalized with floor area, as absolute resale price tends to be more expensive for bigger area.
The first plot design consideration :
Color legend for flat type (3 ROOM, 4 ROOM, 5 ROOM) in plotly allows users to filter accordingly
Hover tip displaying the resale price, floor area, remaining lease, and flat type
Show the code
plot_ly(data = flatpriceclean,
x = ~remaining_lease_years,
y = ~resale_price_persqm,
hovertemplate = ~paste("<br>Resale Price per sqm:", resale_price_persqm,
"<br>Floor Area (sqm):", floor_area_sqm,
"<br>Remaining Lease (Year):", remaining_lease_years),
type = 'scatter',
mode = 'markers',
color = ~flat_type,
marker = list(opacity = 0.6,
sizemode = 'diameter',
line = list(width = 0.2, color = '#FFFFFF'))) |>
layout(title = "Resale Price per flat area increases with remaining lease \nResale transactions, 2022",
xaxis = list(title = "Remaining Lease (Year)"),
yaxis = list(title = "Resale Price per sqm (SGD)"),
legend = list(orientation = 'h',
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor ="top",
x = 0.5,
y = 0.95))Using updatemenus to get a good first glance of all relationships
Show the code
flatpriceclean$flat_model <- fct_reorder(flatpriceclean$flat_model, flatpriceclean$resale_price_persqm)
plot_ly(data = flatpriceclean,
x = ~flat_type,
y = ~resale_price_persqm,
type = "violin",
alpha = 0.3,
marker = list(opacity = 0.6),
box = list(visible = T),
meanline = list(visible = T)) |>
layout(title = "Distribution of resale price by selected factors, \nResale transactions, 2022",
xaxis = list(title = ""),
yaxis = list(title = "Resale Price per sqm (SGD)"),
updatemenus = list(list(type = 'dropdown',
xref = "paper",
yref = "paper",
xanchor = "left",
x = 0.04,
y = 0.95,
buttons = list(
list(method = "update",
args = list(list(x = list(flatpriceclean$flat_type)),
list(xaxis = list(categoryorder = "category ascending"))),
label = "Flat Type"),
list(method = "update",
args = list(list(x = list(flatpriceclean$flat_model)),
list(xaxis = list(categoryorder = "mean ascending"))),
label = "Flat Model"),
list(method = "update",
args = list(list(x = list(flatpriceclean$storey_range)),
list(xaxis = list(categoryorder = "category ascending"))),
label = "Storey Height"),
list(method = "update",
args = list(list(x = list(flatpriceclean$town)),
list(xaxis = list(categoryorder = "mean ascending"))),
label = "Town"),
list(method = "update",
args = list(list(x = list(flatpriceclean$month)),
list(xaxis = list(tickmode = "array")),
list(color = list(flatpriceclean$month))),
label = "Transaction Month")
)
)
)
)3.2 Confirmatory Data Analysis Visualization
The first plot is to investigate other factors that might impact the resale price.
Show the code
ggbetweenstats(
data = flatpriceclean,
x = flat_type,
y = resale_price_persqm,
xlab = "Types of Flat (Rooms)",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different flat types",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
The second plot is to investigate other factors that might impact the resale price.
Show the code
ggbetweenstats(
data = flatpriceclean |>
mutate(storey_range = ifelse(storey_range %in% c("40 TO 42", "43 TO 45", "46 TO 48", "49 TO 51"), "40+", storey_range)),
x = storey_range,
y = resale_price_persqm,
xlab = "Storey Height",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different storeys",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
Thirdly, check the flat_model variables. Filtering for number of observations >= 50
Show the code
flatpriceclean$flat_model <- fct_reorder(flatpriceclean$flat_model, flatpriceclean$resale_price_persqm)
ggbetweenstats(
data = flatpriceclean |>
group_by(flat_model) |>
filter(n() >= 50),
x = flat_model,
y = resale_price_persqm,
xlab = "Flat Model",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different models",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
The town variables are skipped as there are too many variables -> to be considered in the final visualization
Lastly, check the transaction month variables
Show the code
ggbetweenstats(
data = flatpriceclean,
x = month,
y = resale_price_persqm,
xlab = "Month of Transaction",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different \ntransaction months",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
3.3 Visualization of Resale Price by Township
Show the code
town_list <- list()
for (i in 1:length(unique(flatpriceclean$town))) {
town_list[[i]] <- list(method = "restyle",
args = list("transforms[0].value",
unique(flatpriceclean$town)[i]),
label = unique(flatpriceclean$town)[i])
}
annot <- list(list(text = "Select Towns:",
x = 1.41,
y = 0.78,
xref = 'paper',
yref = 'paper',
showarrow = FALSE))Show the code
flatpriceorder <- flatpriceclean[order(flatpriceclean$flat_type), ]
plot_ly(data = flatpriceclean,
x = ~remaining_lease_years,
y = ~resale_price_persqm,
hovertemplate = ~paste("<br>Resale Price per sqm:", resale_price_persqm,
"<br>Floor Area (sqm):", floor_area_sqm,
"<br>Remaining Lease (Year):", remaining_lease_years,
"<br>Town:", town),
type = 'scatter',
mode = 'markers',
size = ~floor_area_sqm,
sizes = c(5, 15),
color = ~factor(flat_type),
marker = list(opacity = 0.6,
sizemode = 'diameter',
line = list(width = 0.2, color = '#FFFFFF')),
transforms = list(list(type = 'filter',
target = ~flatpriceorder$town,
operation = '=',
value = unique(flatpriceorder$town)[1])
)
) |>
layout(title = "Resale Price per flat area increases with remaining lease \nResale transactions by towns, 2022",
xaxis = list(title = "Remaining Lease (Year)",
range = c(40, 100)),
yaxis = list(title = "Resale Price per sqm (SGD)",
range = c(3000, 16000)),
updatemenus = list(list(type = 'dropdown',
xref = "paper",
yref = "paper",
x = 1.4, y = 0.7,
buttons = town_list)
),
annotations = annot
)